Data Transparency: Variances in patient and claims counts explained

The importance of data transparency in healthcare: Variances in patient and claims counts

At Symphony Health, an ICON plc company, we recognise that data transparency is paramount for fostering trust and confidence in the decisions data informs. In an industry where data-driven decisions underpin patient care, business strategies, and policymaking, the true power of data lies not only in its quantity or availability, but in a deep understanding of its origin, definitions, and the interpretations it supports.

Healthcare professionals and clients frequently seek clarity when they observe changes in patient or claim counts after data updates or restatements. This blog explains why these variances occur and how Symphony Health’s commitment to data transparency ensures clients can confidently interpret and utilise their refreshed datasets.

Why do patient and claim counts change?

Restatements in data deliverables can lead to significant and sometimes unexpected changes in patient and claim counts. These adjustments typically arise due to one of three factors:

Data events: any disruption to the inflow of data that affected and continues to reshape the volume and flow of healthcare data
Inclusion of new data sources: a quality data supplier should constantly be looking for and investing in new data sources, such as specialty pharmacy, medical office, and hospital claims data
Changes in privacy requirements: Protecting privacy should be a critical component of a quality supplier. Expert guidance regarding how data can be reported in a compliant manner evolves over time.

Each of these factors influences the data in unique ways that can impact how clients interpret and utilise their deliverables.

Data Events

Data events, for example, can create variances when there are differences in the time periods used for qualifying patients within a dataset.

Patients are included based on defined qualifying events that fall within specific timeframes, such as those related to diagnoses (Dx), prescriptions (Rx), procedures (Px), or surgical interventions (Sx). If the timeframe of a refreshed patient dataset does not match the legacy dataset, the pool of qualifying patients can shift, leading to fluctuations in reported counts. These time-sensitive criteria ensure that only relevant patient data is considered, but they can also mean that previously qualifying patients might be excluded, or new ones included when the dataset’s reference period is updated.

Additionally, modifications to qualifying markets can significantly impact which patients are included. Any changes made to the list of qualifying Rx, Dx, or Px codes can directly influence patient counts. For instance, adding qualifying products, diagnoses, or procedures may result in increased patient counts, while removing such qualifiers will generally lead to reduced counts. For example, if you were receiving a patient dataset containing all solid tumors and decided to narrow the focus to only include breast cancer, then once the data is refreshed, any patients lacking claims indicating breast cancer would no longer be present. This example highlights how modifications to qualifying market criteria can substantially alter patient inclusion and must be carefully considered when analysing restated data deliverables.

Patient age and birth year is another critical factor that can impact patient counts. Patients over the age of 100 at the time of refresh are excluded to meet age limitations, while those aged between 80 and 100 are uniformly reported as age 80. Additionally, patients with invalid birth year data or birth years set in the future are excluded, along with all associated claims. These age-based criteria help maintain data accuracy and compliance but can lead to reduced counts if patients fall outside these thresholds.

Understanding these intricacies is essential for interpreting changes correctly and maintaining trust in the data’s reliability and applicability.

Inclusion of New Data Sources

The addition of new data sources during a data refresh can lead to the inclusion of new patients and potential new physicians, as well as additional claims for both existing and new patients. This process may also bring new plans into the dataset. While these updates enhance the overall depth and comprehensiveness of the data, they can result in noticeable variations when clients compare refreshed deliverables to their legacy counterparts. Understanding these changes is key to appreciating the expanded scope and richer insights provided by updated datasets.

Changes in privacy requirements

To comply with privacy standards such as HIPAA, data suppliers should enforce data remediations that modify or remove sensitive personal information, including birth, death, and residence data, as well as military status, marital status, and employer group plans. These measures are implemented with precision to maintain patient privacy while ensuring the data's continued utility. Key impacts on fact tables include nullification of restricted diagnosis codes and adjustments to Rx numbers within prescription records. Diagnosis records with restricted codes are removed, affecting patient qualification, and similar rules apply to Px and Sx records, resulting in fewer rows where such codes are present.

Dimension tables are also affected, with restricted diagnosis, procedure, and surgical codes being removed, leading to a reduction in the number of rows. Practitioner records are updated to retain only compliant location data, ensuring adherence to privacy regulations. Additionally, sensitive details in the plan and payment sub type dimensions are removed, potentially resulting in fewer matching records. These adjustments are crucial for protecting sensitive information while providing clients with transparent, compliant datasets.

In conclusion, understanding the underlying reasons for dataset changes can be complex, but transparency is essential. Symphony Health is dedicated to ensuring that clients can trust and interpret their data confidently. By providing clarity on data events, source additions, and privacy remediations, we empower clients to discern whether observed changes are typical or not. Transparency is essential when navigating data changes. Choose a provider who shares this value: Symphony Health.

The importance of data transparency in healthcare: Variances in patient and claims counts

Why do patient and claim counts change?

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